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Publication
Machine learning methods applied to the dots and boxes board game
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 5.51 MB | Adobe PDF |
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Abstract(s)
Pontos e Quadrados (Dots and Boxes na versão anglo-saxónica) é um jogo clássico
de tabuleiro no qual os jogadores unem quatro pontos próximos numa grelha para
criar o maior número possível de quadrados. Este trabalho irá inverstigar técnicas de
aprendizagem profunda e aprendizagem por reforço, que torna possível um programa
de computador aprender como jogar o jogo, sem nenhuma interação humana, e aplicar
o mesmo ao jogo Dots and Boxes; a abordagem usada no DeepMind AlphaZero será
analisada. O AlphaZero combina uma rede neural convolucional e o algoritmo Monte
Carlo Tree Search para alcançar um desempenho super humano, sem conhecimento
prévio, em jogos como o Xadrez, Go, e Shogi.
Os resultados obtidos permitem aferir sobre a adequação da abordagem ao jogo
Pontos e Quadrados.
Dots and Boxes is a classical board game in which players connect four nearest dots in a grid to create the maximum possible number of boxes. This work will investigate deep learning techniques with reinforcement learning to make possible a computer program to learn how to play the game, without human interaction, and apply it to the Dots and Boxes board game; the approach beyond DeepMind AlphaZero being taken as the approach to follow. AlphaZero makes a connection between a Convolutional Neural Network and the Monte Carlo Tree Search algorithm to achieve superhuman performance, starting from no a priori knowledge in games such as Chess, Go, and Shogi. The results obtained allow to measure the approach adequacy to the game Dots and Boxes.
Dots and Boxes is a classical board game in which players connect four nearest dots in a grid to create the maximum possible number of boxes. This work will investigate deep learning techniques with reinforcement learning to make possible a computer program to learn how to play the game, without human interaction, and apply it to the Dots and Boxes board game; the approach beyond DeepMind AlphaZero being taken as the approach to follow. AlphaZero makes a connection between a Convolutional Neural Network and the Monte Carlo Tree Search algorithm to achieve superhuman performance, starting from no a priori knowledge in games such as Chess, Go, and Shogi. The results obtained allow to measure the approach adequacy to the game Dots and Boxes.
Description
Keywords
Adversarial search Machine learning Deep learning Reinforce-ment learning Dots and boxes Rede neural artificial Rede neural convolucional Jogos Alphazero Deepmind Jogos de tabuleiro Auto aprendizado